Chaotic Flow by Joel York » SaaS Metrics

SaaS Business Model – On the Cloud, the Customer is King

Joel York — Mon, 19 Apr 2010 17:32:57 +0000

This may sound like evangelical cloud mumbo jumbo, but I’m actually alluding to the central importance of the ongoing customer relationship in the SaaS business model and its direct linkage to the financial success of a SaaS business. In the SaaS business model, the ongoing customer relationship is a continuous source of revenue, cost, business activity and risk. This contrasts sharply with traditional software where the short-term sales transaction has always taken center stage.

The fundamental shift in value from copies to customers
turns the economics of licensed software upside down,
and is still an elusive financial concept for the industry
when evaluating SaaS business value, profitability
and capital efficiency.

A traditional software vendor makes and sells perpetual license copies, whereas a SaaS business makes and sells ongoing service subscriptions. Each new SaaS customer brings a new thread of recurring revenue and cost which are woven into the larger tapestry of customers to create the total SaaS recurring revenue stream and associated SaaS cost of service. This fundamental shift in the unit of value from copies to customers turns the economics of licensed software upside down, and is still an elusive financial concept for the industry when evaluating SaaS costs, profitability, business valuation and capital efficiency.

Customers vs. Copies

In traditional licensed software, value is equated to the intellectual property of the code, and is monetized using copyrights in a fashion similar to books, music, and movies. Volume is measured in licensed copies and value is measured by the price of a license. But in the SaaS business model, volume is measured by the number of customer subscriptions and value is measured by recurring revenue. A software vendor invests in developing code, and then operates a sales and marketing infrastructure that scales to sell more licensed copies. A SaaS business invests in acquiring customers, and then operates a service delivery operation that scales to service customer subscriptions. Mathematically….

software profit = ( license price – transaction cost ) x copies – R&D costs

SaaS profit = ( recurring revenue – recurring service cost ) x customers – acquisition cost

Operational costs that are fixed relative to the number of license copies sold in the software business model are now variable relative to the total number of customers in the SaaS business model, such as product development which in SaaS becomes part of the recurring service cost and extends far beyond the initial source code investment to the entire business infrastructure (see note below: Breaking Down Total SaaS Cost of Service and SaaS Do #5 – Build the Business into the Product). Alternatively, sales and marketing costs that are variable relative to the number of license transactions in the software business model are suddenly fixed customer acquisition costs relative to the total number of customers in the SaaS business model, variable instead with the number of new customers.

Happy Customers Drive SaaS Business Model ROI

Capital efficiency is about achieving the highest possible return on investment (ROI). High ROI in turn is achieved by minimizing the up front fixed costs of a business and then maximizing margin by scaling revenue well in excess of variable costs. Like putting a down payment on a rental property, and then making sure the the rental income covers the operating expenses and interest payments. In the license software model, capital efficiency is measured by product ROI, but in the SaaS business model the best measure of capital efficiency is customer ROI, or the average customer rate of return (aka Joel’s SaaS Magic Number).

SaaS Customer ROI	=	ARR – ACS
		CAC

Where “ARR” is the average recurring revenue per customer, “ACS” is the average recurring cost of service per customer, and “CAC” is the average customer acquisition cost. Software ROI is achieved by selling more copies to cover your R&D investment. SaaS ROI is achieved by acquiring more customers and maximizing customer lifetime value through upselling and retention to cover your customer acquisition cost.

In the end, it all has to add up to happy customers. Service subscriptions are perishable. They can’t be copied and stored like a book or a movie or a software CD. A SaaS business without customers simply doesn’t exist—like the sound of a tree falling in the woods with no one around to hear it. In the SaaS business model, the customer really is king.

Breaking Down Total SaaS Cost of Service

Total SaaS cost of service is comprised of recurring service cost and customer acquisition cost. Both of these SaaS cost categories are variable in nature, because the SaaS business model has two primary drivers of variable costs (total customers and new customers) as opposed to the single primary driver of the licensed software model (transactions). Albeit both variable, customer acquisition costs are fixed relative to recurring service costs. And, it is worth noting that a very mature software business starts to take on this dual cost driver characteristic of the SaaS business model when maintenance revenue begins to dominate new license revenue. However, this duality is present in the SaaS business model from the very first customer subscription.

SaaS Costs – Variable vs. Fixed
I am of the opinion that there are very few fixed SaaS costs, at least in the long run. Most all operational SaaS costs can be included in either recurring service cost or customer acquisition cost. Once a SaaS business has launched the initial 1.0 version of its product and approaches a nominal efficient scale in terms of customers, you will be hard pressed to find a cost that does not scale roughly with either the total number of customers (recurring service cost) or the number of new customers (customer acquisition cost).

Customer acquisition costs are easily identified as direct sales and marketing expenses, but recurring service costs are often hidden in ostensibly fixed categories like product development and administration. While these costs are arguably fixed in the software license model, they scale quite consistently with the number of customers in the SaaS business model. For example, accounts receivable costs scale with renewals and accounts payable scale with operational expenses, all of which in turn scale largely with the number of customers.

After the 1.0 release, product development will spend most of its time adding features for upselling, enabling customer self-service, increasing system performance, expanding infrastructure and fixing bugs, all of which are costs incurred in the service of customers. In fact, once the nominal efficient scale of the operation is reached, they are likely to scale more or less linearly with the number of customers. Only in the case of creating and introducing a new SaaS product for an entirely new market are any operational SaaS costs likely to be fixed relative to the total number of customers, simply because there are no customers as yet.

SaaS Costs – Accounting vs. Economic
Variable recurring service cost is often equated with the accounting measure for cost of goods sold (COGS), which usually includes the most direct product delivery costs, such as infrastructure hardware and software, network fees, etc. However, there are two problems with this: 1) COGS accounting rules were designed with manufactured goods in mind and there is wide variability in what SaaS companies include or don’t include in it and 2) pretty much no matter what is included, the COGS measure is not wide enough to equate it to the variable SaaS costs for recurring service.

There is only one question you have to ask to determine where to place each component of total SaaS cost of service.

In the long run, does the cost scale with total customers (recurring service cost),
new customers (customer acquisition cost), or neither (fixed SaaS costs).

Fixed SaaS costs should be determined by what is left over after all recurring service costs and customer acquisition costs are identified. And, accounting costs are well, just accounting costs.

SaaS Customer Lifetime Value Drives SaaS Company Value

Joel York — Tue, 06 Apr 2010 17:05:15 +0000

For the life of me, I still can’t fathom how VCs value early stage startups. I think it is one part business logic and five parts voodoo (no offense to my VC friends, but you know what I mean). However, I’m guessing most SaaS-focused VCs know this SaaS company valuation principle in their gut even if they can’t reduce it to a financial projection: SaaS Customer Lifetime Value Drives SaaS Company Value. For what is a company if not the sum of its customers?

The link between SaaS CLTV and SaaS company valuation
arises naturally from the SaaS subscription model
where topline company revenue emerges
as the sum of individual customer revenue streams.

This is the final post in a series that aims to uncover the mysteries of SaaS financial metrics from a new angle, a little mathematics. In this installment, I’ll explore the important SaaS metric of customer lifetime value (CLTV) and introduce a new SaaS financial metrics rule-of-thumb that relates SaaS CLTV to SaaS company value.

Buyer Beware: The ideas presented in this installment of the SaaS metrics series are not meant to offer methods or models for evaluating the value of any particular SaaS company, but rather to offer credence to the gut reaction above. Use the formulas herein at your own discretion. Chaotic Flow, i.e., me, accepts no liability thereof.

SaaS Metrics Rule-of-Thumb #10
SaaS Customer Lifetime Value Drives SaaS Company Value

The financially accepted method for estimating the value of company is the calculation of the discounted value of its future cash flows, or net present value. Now, I’m not saying that anyone in InternetWorld actually uses this method, but for the sake of this series on SaaS financial metrics, this is the only approach that lends itself to mathematical analysis. So, I am stuck with it.

The exact mathematical statement of SaaS Metric Rule-of-Thumb #10 above is expressed as follows (see SaaS Metrics Math Notes below for the derivation):

SaaS Company NPV = CLTV x NEW_LTV

Where CLTV is the average SaaS customer lifetime value and NEW_LTV is an analogous measure for the lifetime value number of customers that represents the discounted number of new customers acquired by our SaaS company during its lifetime, using the standard NPV formula.

NEW_LTV = NEW₁/( 1 + i ) + NEW₂/( 1 + i )² +…+ NEW_N/( 1 + i )^N

Here NEW_n is the number of new customers acquired during the period n. NEW_LTV is the present value of all the new customers that the company will ever acquire discounted by how long it takes to acquire them. If market penetration is slow, then NEW_LTV might be less than 10% of the total number of customers at maturity. Whereas, if market penetration is fast and absolute, then NEW_LTV will be closer to the total number of potential customers.

The formula above elegantly encompasses three critical questions asked by SaaS VCs:

What is the value to the customer? (CLTV)
How big is the market? (NEW_N)
How fast can you get there? (NEW_LTV)

The link between SaaS CLTV and SaaS company valuation arises naturally from the SaaS subscription model where topline company revenue emerges as the sum of individual customer revenue streams. In this regard, this new SaaS metrics rule-of-thumb linking customer value to company value is really a corollary of SaaS Metrics Rule-of-Thumb #4 – Company Time to Profit Follows Customer Break-Even, which had I been more esoteric (as if this SaaS metrics series isn’t already esoteric enough!), I might have stated as SaaS company cash flow follows SaaS customer cash flow (most SaaS CEOs can validate this version of the principle without any need for advanced mathematics).

While CLTV can be calculated for each individual customer, it is more commonly quoted as a generalized metric that describes the average SaaS customer as follows:

CLTV = ARR_LTV – ACS_LTV – CAC

CLTV = (ARR₁ – ACS₁)/( 1 + i ) + (ARR₂ – ACS₂)/( 1 + i )² +…+ (ARR_N – ACS_N)/( 1 + i )^N – CAC

Where “ARR_LTV” is average lifetime recurring revenue per customer, “ACS_LTV” is the average lifetime recurring cost of service per customer, and “CAC” is the average customer acquisition cost. ARR_n and ACS_n are the average customer recurring revenue and cost of service for the period n, and “i” is the cost of capital, which for VC funded startups is 30%+.

I’ve explicitly separated revenue from costs, because costs are variously omitted from CLTV depending on the application. For example, if I want to compare the “lifetime value” of a customer in terms of net contribution vs. the cost of acquiring a customer, then I omit the acquisition cost from the CLTV calculation. Or, if I wan’t to measure the “lifetime value” as the money a customer pays during its lifetime, then I’d only keep ARR_LTV. The formula as written is the total net present value of all cash flows related to acquiring and servicing the average SaaS customer.

Using this “fully loaded” CLTV, we can also give a useful variation to the formula for total SaaS company value when the company already has current customers, C_old, as follows:

NPV = CLTV x NEW_LTV + ( CLTV + CAC ) x C_old

NPV = CLTV x NEW_LTV + ( ARR_LTV – ACS_LTV ) x C_old

Where in this case NEW_LTV represents all the future customers still yet to be acquired and C_old represents the number of current customers. Notice that using our “fully loaded” CLTV we must add CAC back for current customers, because it is a sunk cost. If new customers are somehow different from old customers, or CLTV changes over the customer lifetime, then the old customer CLTV / CAC values would also be different from the new.

In the simple case of constant recurring payments and costs per period, CLTV for the average customer is calculated as follows:

CLTV = (ARR – ACS)/( 1 + i ) + (ARR – ACS)/( 1 + i )² +…+ (ARR – ACS)/( 1 + i )^N – CAC

CLTV = ( ARR – ACS )/i x ( 1 – ( 1 + i )^-N ) – CAC

When there is no limit placed on the number of payments (an annuity).

CLTV = (ARR – ACS )/i – CAC

And, if we throw churn and viral growth into the mix CLTV becomes.

CLTV = ( ARR – ACS – gCAC )/( i + a – g ) – CAC

with the constraint g < i + a

Where “ARR” is the average recurring revenue per customer, “ACS” is the average recurring cost of service per customer, “a” is percentage churn rate, “g” is the viral growth rate. This last formula should be used with extreme caution in high growth, big g scenarios as treating viral growth as something that extends indefinitely is unrealistic. Sooner or later you hit the market limit and growth slows. Which is why the formula blows up when g = i + a.

SaaS Financial Metrics Math Notes

The idea that the character of a SaaS company is determined in large part by its average customer is a theme played out in many of the SaaS metrics rules-of-thumb. The underlying cause of this general principle is the recurring revenue subscription model. Unlike customers of licensed software, SaaS customers ease into their relationships with SaaS vendors through many repeat purchases. Each new SaaS customer brings a new thread of recurring revenue, which is woven into the larger tapestry of customers to create the total SaaS company recurring revenue stream. Using the continuous SaaS metric model, the formula for total recurring revenue is as follows:

Total Recurring Revenue = TRR(t) = ∫₀^t New(τ)ARR(t – τ)dτ

Where New(t) is the number of new customers acquired by the SaaS company over time, and ARR(t) is the recurring revenue of the single average customer over time. The integral sum adds up the recurring revenue from each new customer acquired from the beginning to today (0 to t), starting each new customer on its own recurring revenue path beginning at the time it was acquired, τ. For the math heads, it’s a convolution of New(t) and ARR(t).

In the continuous model, the net present value of company recurring revenue is given by the following formula:

NPV Total Recurring Revenue = TRR_LTV = ∫₀^∞ TRR(t)e^-itdt

Where “i” is the cost of capital. Again, for the math heads in the audience (and I can’t much imagine there is anyone else left at this point), this is a standard Laplace Transform. Using a known property of Laplace Transforms that the transform of a convolution equals the product of the two individual transforms, we can simplify the expression for TRR_LTV as follows:

TRR_LTV = ∫₀^∞NEW(t)e^-itdt x ∫₀^∞ARR(t)e^-itdt

TRR_LTV = NEW_LTV x ARR_LTV

Which is starting to look a lot like SaaS Metrics Rule of Thumb #10 above. Had we started with ARR – ACS and thrown in CAC from the start, we would have had the following:

TRR_LTV – TCS_LTV – TCAC = NEW_LTV x ( ARR_LTV – ACS_LTV – CAC )

SaaS Company NPV = NEW_LTV x ( ARR_LTV – ACS_LTV – CAC )

SaaS Company NPV = NEW_LTV x CLTV

Voila! SaaS Rule-of-Thumb #10 – SaaS Customer Lifetime Value Drives SaaS Company Value.

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Metrics – Joel’s Magic Number for SaaS Companies

Joel York — Tue, 09 Mar 2010 14:15:58 +0000

One of the mysteries I hoped to solve when I embarked upon this little SaaS metrics mathematical journey was the reality behind “The SaaS Company Magic Number” introduced by then Omniture CEO, Josh James, and immemorialized by my pal Lars Leckie over at Hummer Winblad. In principal, this number tells you how aggressively you should be spending to build up your customer base: “the key insight is that if you are below 0.75 then step back and look at your business, if you are above 0.75 then start pouring on the gas for growth because your business is primed to leverage spend into growth. If you are anywhere above 1.5 call me immediately.”-Lars Leckie’s Blog

In this installment of the SaaS metrics series, I will show why this benchmark works, and introduce what IMHO is the single most important SaaS financial metric for measuring the overall health of a SaaS business. Now, being as Josh and Lars have already laid claim to “The” SaaS Magic Number, I really have no alternative but to put all humility aside and dub my latecomer SaaS metric as “Joel’s” SaaS Magic Number (After all, I did similarly snag “The” Top Ten Do’s and Don’ts of SaaS, so it’s all good.)

SaaS Metrics Rule of Thumb #9 – Joel’s SaaS Magic Number

The truth be told I feel a bit guilty about the name, because Joel’s SaaS Magic Number is neither magic, nor mine really. It’s simply the the average customer rate of return, or rather the inverse of the average customer baseline break-even, 1/BE₀, that has so consistently popped up as a driver and a constraint in the SaaS metrics rules-of-thumb.

J	=	ARR – ACS
		CAC

Joel’s SaaS Magic Number

Average Customer Rate of Return

Where “ARR” is the average recurring revenue per customer, “ACS” is the average recurring cost of service per customer, and “CAC” is the average customer acquisition cost. Customer rate of return is powerful, because it measures the economics that make a SaaS business work (or not), whereas the individual revenue and cost metrics are simply accounting figures that in isolation say little about the health of the business. Let’s recap some of the things we know about this nifty magic number from earlier SaaS metrics rules-of-thumb.

Joel’s SaaS Magic Number Rules-of-Thumb
J	[ ARR - ACS ] ÷ CAC	average SaaS customer rate of return
1/J	BE₀	best case SaaS company time to profit
limiting	g = a = J	maximum, profitable rate of growth g or churn a
approaching	g ⇒ J or a ⇒ J	dramatically delays SaaS time to profit
exceeding	g ≥ J or a ≥ J	SaaS company will never be profitable
increasing	⇑J by ⇑ARR or ⇓TCS	upselling & lower TCS accelerate profitability
recommended	J > g + a	in high growth & churn scenarios
benchmark	J ≥ 50%	per year is generally very healthy

Intuitively, the long run profitability of SaaS companies requires
the recurring contribution of current customers to cover the acquisition cost of new customers,
therefore the average customer rate of return for SaaS companies
must exceed both the current customer churn rate and the new customer growth rate.

So, what is a good value for Joel’s SaaS Magic Number? Well, I don’t think anyone could argue with 1, a valiant goal, but rather optimistic. If your average customer rate of return is 100%, chances are you don’t need VC money, because you can bootstrap your way to the top given that your customers pay for themselves within the first year. Personally, I’m prejudiced toward profitable growth in the long run, not just growth (call me old fashioned). In which case, I’d recommend shooting for something in the J ≥ 50% per year range, implying a minimum time to profit of 2 years (probably more like 4 years with growth and churn) and the ability to grow annually at upwards of 50%.

Customer rate of return is powerful, because it measures
the economics that make a SaaS business work (or not),
whereas the individual revenue and cost metrics
are simply accounting figures that in isolation
say little about the health of the business.

At the very least, you should set a goal for average customer rate of return that significantly exceeds your combined churn and target growth rates, J > a + g, unless you are prepared to burn cash for a very, very long time and build up a very, very large accumulated loss deficit on your balance sheet. But, focusing exclusively on growth to the detriment of profit is a dangerous game in SaaS. Most SaaS markets are not as wide-open as enterprise software markets were twenty years ago. Personally, I believe there is very little a weak executive team can do for $110M in expense at a $10M loss, that a strong executive team can’t do better for $90M in expense at a $10M profit. To each his own.

I’ll let the interested reader refer to Lars’ original blog post for the specifics, but after a little comparison I’ve concluded that “The” SaaS Company Magic Number is roughly equivalent to following ratio in my SaaS metrics model.

The SaaS Company Magic Number = ARR ÷ CAC

(for newly acquired customers with ARR measured annually)

Thus, the difference between Joel’s SaaS Magic Number and The SaaS Magic Number is the explicit inclusion of recurring cost of service, giving IMHO a stronger metric for the financial health of SaaS companies that includes the full measure of both recurring revenue and total cost of service. In relation to The SaaS Magic Number we have the following:

Joel’s SaaS Magic Number = [ ARR - ACS ] ÷ ARR x The SaaS Magic Number

Joel’s SaaS Magic Number = Contribution Margin x The SaaS Magic Number

Let’s plug in some numbers and see what we get. If we want a quick and dirty comparison of The SaaS Magic Number to Joel’s SaaS Magic Number, then the easiest approach is to assume a contribution margin of 50%. This isn’t so outrageous given the cost structure of many SaaS/software companies that even in the early stages tend to spend about 50% on sales and marketing (CAC) and 50% on everything else (ACS).

When Contribution Margin = 50%

Joel’s SaaS Magic Number = The SaaS Magic Number / 2

Aha! Now we have a rhyme to the reason of why Lars just might want to talk to you if The SaaS Magic Number is > 1.5, because you are primed for a profitable, annual viral growth rate of up to 75%, something VCs like very much. However, I have to take issue with “pouring on the gas” if your The SaaS Magic Number is simply above .75 . In this example, your average customer rate of return is about 38%., giving you a best case time to profit of 2.7 years. In reality, if your annual growth is approaching 38% (which your investors will very much desire) and your contribution margin is anything less than 50% (very likely in the early stages), then your real time to profit is probably double or triple that (8 years!), because you aren’t even covering your acquisition costs let alone your total cost of service. Depending on your actual contribution margin, you may very well be burning through wads of cash by stepping on the gas. Personally, I’d up this requirement to a clean The SaaS Magic Number of > 1.0 with the added requirement that ARR ≥ 2 x ACS, so that your contribution margin can quickly recover acquisition costs. This is consistent with the prevalent SaaS metrics conventional wisdom that you’d like to recover your customer acquisition cost in the first 12 months, but with the added requirement that you need to pay for your ongoing service costs as well.

Almost done! There is one more post in this SaaS metrics series, because clearly we can’t stop until we reach SaaS Metrics Rule of Thumb #10 and have a complete SaaS metrics top ten list. The next and final post in the SaaS metrics series entitled SaaS Customer Lifetime Value Drives SaaS Company Value will take things up a level to examine the highest level SaaS financial metric of company valuation, and it has some fun, no-holds-barred extra credit math notes for the math heads. Stay tuned!

SaaS Metrics Math Notes: What’s YOUR SaaS Average Customer Rate of Return?
The theory is only useful if we put it into practice, so today’s SaaS Metrics Math Note includes the homework assignment to calculate the average customer rate of return for your SaaS business (your very own SaaS magic number!). In practice, calculating average customer rate of return requires a few decisions about how you’re going to measure recurring revenue and costs, but the basic formula for calculation is as follows.

[ Total Recurring Revenue - Total Recurring Costs ] ÷ Total Number of Customers

Total Acquisition Costs ÷ Number of New Customers

Total Acquisition Costs should include all sales, marketing and overhead expenses directed at new customer acquisition within a given period, and Total Recurring Costs should include just about everything that is left over in the same period, including everything that enables you to service your customers from rent to support staff, even accounting because you gotta send out the bill. You can dance around the idea of fixed vs. variable costs, but in most SaaS companies pretty much all costs are variable in a roughly 3+ month time-frame. Also, note that the average customer rate of return time-frame will be the same one that you choose for your recurring costs, i.e., percent per month/quarter/year respectively.

I recommend making two charts, one with the average customer rate of return (you can also include the other two relevant SaaS metrics of growth and churn for comparison) and a second with the component values of ARR, ACS and CAC. This example highlights the positive impact of both lowering total cost of service (decreasing ACS/CAC) and upselling (increasing ARR) on average customer rate of return as implied in SaaS Metrics Rules-of-Thumb 6, 7 & 8.

Whether you choose GAAP, cash or any number of other variations as your SaaS metrics basis for calculation is probably fine as long as you are consistent throughout. Personally, I prefer cash.

Plus, you’ll want to pick a measurement period that will highlight the long term trend over short term random fluctuation, because these profitability and growth SaaS metrics are mid-to-long term measures. One month is probably too short and one year is probably too long. So, some sort of moving average around 3-6 months is probably best for most SaaS companies. You can also get fancy with things like customer segmentation to see how the average customer recurring rate of return varies by segment. Consider that extra credit!

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Revenue – The Beauty of Upselling and Upgrades

Joel York — Tue, 02 Mar 2010 16:00:01 +0000

Too much churn, you lose money. Grow too fast, you lose money. Customer acquisition cost too high, you lose money. Recurring cost of service too high, you lose money. Just shoot me now! How on earth do you make money in SaaS?

If you’ve been following this series on SaaS metrics, then you’ve probably worked your way up to just such a level of frustration. Because, that pretty much sums up the last four SaaS metrics rules-of-thumb on SaaS profitability:

It just can’t be! This is high tech. This is the Internet! Software, not hardware. SaaS can’t just be a low-cost commodity business. There must be a value-based approach. A SaaS revenue solution!

There is. But, it doesn’t mean you can ignore the fundamentals. Driving down total cost of service through automation and economies-of-scale is fundamental. SaaS executives that ignore this put the long term profitability of their SaaS businesses in peril. But, enough on lowering SaaS costs. Let’s talk increasing SaaS revenue!

The previous post in this SaaS metrics series entitled Growing Up Poor – How Foolish SaaS Companies Lose Money was a bold attempt to once and for all solve the mystery of why seemingly successful SaaS companies lose money…in excruciating detail (sorry for that). The cornerstones of the analysis were the relationships established between the values of churn, “a”, growth, “g”, and the baseline average customer break-even time, “BE₀” as follows:

BE₀ = CAC ÷ [ ARR - ACS ]

Higher	gBE₀ or aBE₀	means longer time to profit.
When	gBE₀ ≥ 1 or aBE₀ ≥ 1	the SaaS company will never be profitable.
Hence	g = a = 1/BE₀	is the maximum, profitable rate of growth or churn.

In the formula above, “CAC” is the average acquisition cost per customer, “ARR” is the average recurring revenue per customer, “ACS” is the average recurring cost of service per customer, and the 0 attached to the BE is intended to indicate the absence of churn, i.e., the baseline break-even.

These constraints were the basis of SaaS metrics rules-of-thumb #6 and #7 which claim that both growth and churn, respectively, increase pressure to reduce total cost of service (CAC and ACS) in order to accelerate profitability (reduce BE₀) and to ensure that profitability is even possible (gBE₀,aBE₀ < 1). However, the viable SaaS revenue strategy of increasing average recurring revenue (ARR) without increasing total cost of service was carefully set aside for later examination. Leading us to...

SaaS Metrics Rule-of-Thumb #8
Upselling and Upgrades Accelerate SaaS Profitability

What exactly does it mean to increase average recurring revenue without increasing average customer acquisition cost or average recurring cost of service? First, no additional customer acquisition cost means that we must increase recurring revenue from current customers. Second, no increase in average recurring cost of service means that we must do so in the normal course of business with very little extra effort, preferably through customer self-service (self-selling!). In other words, SaaS companies can accelerate time to profit by upselling and upgrading current customers, but only if it follows an exceptionally low cost purchase process distinct from the new customer acquisition process. The chart below depicts the impact of upselling and upgrades on SaaS time to profit.

Upselling and upgrades leverage the initial investment of customer acquisition cost
to accelerate SaaS time to profit by countering the delays of both growth and churn.

The chart above depicts the effect of upselling on the two uglier examples presented in the last SaaS metrics series installment where times to profit where 8 years and never, respectively. These are determined by the intersections of the blue line, which depicts the recurring contribution (recurring revenue minus recurring cost of service) of a SaaS company with a growth rate of 20%, zero churn, and an average recurring contribution per customer of $1000 – $500 = $500 per year, with the two red lines representing average customer acquisition costs of $2000 and $2,750 respectively.

The green line depicts the impact of increasing SaaS revenue over time through upselling and upgrades. With upselling, the 8 year time to profit scenario is accelerated to just under 5 years, and never has been magically transformed to about 7.5 years as represented by the intersection of the green line with the same red CAC lines. The green line represents the recurring contribution of the same SaaS company, but with an upselling strategy that increases contribution margin, (ARR – ACS), without incurring any one-time acquisition costs by $75 each year, or an upsell percentage of 15%. (see SaaS Metrics Math Notes below)

Finally, some light at the end of the tunnel! Pure upselling as defined here, reduces BE₀ by increasing recurring revenue without significantly increasing total cost of service, i.e., money for nothing. What could be more strategic!

Average SaaS Customer Rate of Return
It’s enlightening to rewrite the second formula above which restricts the percentage rates of both churn and growth to provide a clearer focus on SaaS recurring revenue:

1/BE₀	=	ARR – ACS	≥	a , g
		CAC

In this format, we can see that increasing average recurring revenue per customer, ARR, to reduce baseline break-even, BE₀, is equivalent to increasing the average rate of return return on our investment of customer acquisition cost. That is, the average recurring contribution, ARR – ACS, can be interpreted as the interest earned on the investment of customer acquisition cost, CAC. Since the SaaS recurring revenue model implies that contribution from current customers must cover the cost of acquiring new customers, then the rate of return on current customers must exceed both the percentage growth and churn rates, otherwise the old can’t fund the new. Upselling and upgrades increase the average rate of return on current customers, accelerating SaaS time to profit and enabling a SaaS company to absorb higher percentage rates of growth and churn.

Two great examples of upselling and upgrades in practice are Salesforce.com and Xignite. Salesforce.com has standard user-based subscription pricing, but then breaks its subscriptions into carefully designed modules of increasing functionality. If you’ve ever been a salesforce.com customer, then you know that 90% of the upgrade process consists of you, as the customer, repeatedly bumping into the limits of your current subscription. When you need more users, or you need the capabilities of enterprise over professional, then you go online or pick up the phone and order them. The cost to salesforce.com is minuscule compared to the original customer acquisition cost (which includes not only customers, but all the prospects that didn’t buy). Salesforce.com is a master of application discovery, which is the process of letting less experienced customers discover for themselves the value of more advanced product capabilities.

Xignite is a cloud services provider of on-demand market data (Who was that guy?!). Unlike monolithic end-user applcations such as Salesforce.com, Xignite’s Web services can be purchased separately and mixed and matched at will. The company offers a market data catalog of more than 50 services with usage-based subscription plans, all of which can be easily purchased online. The range of potential recurring revenue from the lowest plan for a single service to the largest plan for all services gives an upsell potential for each customer of around 1000:1 or total potential upsell of 100,000%!

This post concludes the mini-series on SaaS profitability, but it is not the end of the SaaS metrics series. Surely, we’re only up to SaaS Metrics Rule-of-Thumb #8 and we can’t possibly stop until we round it off to #10. The next post in the series entitled Joel’s Magic Number for SaaS Companies will focus more deeply on what IMHO is the most important SaaS metric of all: average customer rate of return, and will go beyond the math to provide some simple benchmarks to help your SaaS business grow up healthy and profitable.

SaaS Metrics Math Notes
The approach used to model upselling and upgrades is considerably more conservative than that used to model customer growth. In particular, no viral market mechanism exists to promote exponential growth of ARR as with customer acquisition growth or churn. Moreover, upselling is severely limited by the size of the customer and breadth of the product offering. Therefore, a linear growth model based on the original subscription ARR was used.

In addition, the example assumes an increase in recurring contribution with no improvement in margin, i.e., the add-on business incurs recurring costs proportionate with its recurring revenue, only new acquisition costs are avoided. A more aggressive model might have simply increased recurring revenue and avoided additional recurring costs of service. Specifically, the formula for upselling used above is as follows:

ARR(t) – ACS(t) = (ARR₀ – ACS₀) x ( 1 + ut )

Where “u” is the percentage upsell rate. Integrating the above contribution over time and setting the resulting profit equal to zero gives the upsell accelerated time to break-even:

(ARR₀ – ACS₀) x ( BE_u+ uBE_u²/2 ) – CAC = 0

uBE_u²/2 + BE_u – BE₀ = 0

Using the quadratic formula (which I honestly can’t remember using in the past 20 years) this gives the formula for break-even under upselling:

BE_u = [ -1 + ( 1 + 2uBE₀ )^1/2 ] / u

In the example above BE₀ = 4 years and 5.5 years, respectively. With u = 15%, the impact of upselling is to accelerate break-even to BE_u = 3.2 years and 4.2 years, respectively. Thereby increasing the respective tolerable rates of growth and churn from 25% = 1/4 and 18% = 1/5.5 to 31% = 1/3.3 and 24% = 1/4.2. Since the growth rate in the example is 20%, we can see that upselling takes the latter scenario from unprofitable to profitable as indicated in the chart.

Check out the rest of the SaaS Metrics Rules-of-Thumb

Growing Up Poor – How Foolish SaaS Companies Lose Money

Joel York — Tue, 23 Feb 2010 13:48:36 +0000

Over and over again, I see exciting headlines for SaaS companies like this…

That when I dig a little deeper, I end up having my bubble burst by a dismal financial statement that looks something like this…

Where is all the money going? Why do so many successful SaaS companies, startup and public alike, have such a difficult time turning a profit?

This is the fourth post in a series on SaaS metrics that will once and for all solve the mystery of why seemingly successful SaaS companies lose money by using a little mathematics. But, before I jump into the technical details, here is the short answer: foolish SaaS companies don’t seriously tackle the problem of reducing Total Cost of Service through automation and economies-of-scale, and they foolheartedly chase after the Recurring Revenue Mirage.

In the last installment in this series, I introduced the following two SaaS metrics rules-of-thumb:

Which in concert make the claim that the time to profit for a growing SaaS company will always be greater than the baseline break-even time for the average SaaS customer.

t_profit ≥ BE₀

BE₀ = CAC ÷ [ ARR - ACS ]

Where “CAC” is the average acquisition cost per customer, “ARR” is the average recurring revenue per customer, “ACS” is the average recurring cost of service per customer, and the 0 attached to the BE is intended to indicate the absence of churn, i.e., the baseline break-even.

In this post, we’ll zero in on the interplay of growth and profitability with customer acquisition cost and recurring cost of service (collectively total cost of service), starting with…

SaaS Metrics Rule-of-Thumb #6
Growth Creates Pressure to Reduce Total Cost of Service

We know from the earlier SaaS Metrics Rule-of-Thumb #2 – New Customer Acquisition Growth Must Outpace Churn, that successful SaaS companies must strive to grow new customer acquisition at a percentage growth rate, “g”, that exceeds the churn rate. This growth model is quite general. It applies equally well when growth is slow and plodding with a small value of g and when growth is rapid and extremely viral with a high value of g. The only requirement is that our successful SaaS company grow consistently from year to year, which is of course what we all want. In this case, the relationship above between SaaS company profitability and SaaS customer break-even can be made much more exact (see math notes below):

Higher	g x BE₀	means longer time to profit.
When	g x BE₀ ≥ 1	the company will never be profitable.
Hence	g = 1/BE₀	is the maximum, profitable rate of growth.

Since no SaaS company in this universe will throttle back on growth, the only acceptable strategy is to reduce the value of BE₀ such that g x BE₀ is significantly less than 1, otherwise it may be a looooooong time to profit, perhaps well after the company stops growing altogether (gasp!).

This creates pressure to reduce total cost of service by lowering the average customer acquisition cost, CAC, and lowering the recurring cost of service, ACS. Since growth is the culprit, the surest and most effective defense is to fight fire with fire by reducing total cost of service through automation that provides cost-lowering economies-of-scale. (The one other viable solution, increasing ARR without increasing CAC or ACS will be the topic of the next post in the series entitled SaaS Revenue – The Beauty of Upselling and Upgrades) This latest SaaS metric rule-of-thumb is visually depicted in the chart below.

New customer acquisition costs are paid with the recurring contribution of current customers.
If a SaaS company grows rapidly, growing acquisition costs can outpace
the build-up of recurring contribution, such that profitability is impossible.

As outlined in the previous post on SaaS profitability, time to profit occurs when the rate of profit is zero:

[ ARR - ACS ] C(t_profit) – CAC ΔC_new(t_profit) = 0

Or, when recurring contribution, [ ARR - ACS ] x C, equals new customer acquisition costs CAC x ΔC_new. The blue line in the chart above shows the recurring contribution for a SaaS company with a customer acquisition growth rate of 20%, zero churn, and a recurring contribution per customer of $1000 – $500 = $500 per year. The three red lines represent CAC values of $1250, $2000 and $2,750 respectively. In these three scenarios, the baseline break-even values are 2.5 years, 4 years and 5.5 years, giving values for g x BE₀ of .5, .8 and 1.1 respectively. We can see that in each case the actual time to profit values of 3.5 years, 8 years and never significantly exceed the respective baseline break-even values. And, in the case of g x BE₀ = 1.1 profitability is unattainable. If we add a churn rate of 25% to the mix, such that our growth is drowned out by churn, then we end up paying a hefty bill on both ends of the customer lifecycle, and profitability becomes even more difficult to attain as depicted in the chart below.

Perhaps not so surprisingly, given the mirror image relationship between viral growth and churn explored in the second post in this series, the impact of churn on profitability mirrors that of growth.

Higher	a x BE₀	means longer time to profit.
When	a x BE₀ ≥ 1	the company will never be profitable.
Hence	a = 1/BE₀	is the maximum, profitable rate of churn.

In the example depicted in the chart above, churn has pushed our growth delayed times to profit of 3.5 years, 8 years and never, out to 5.8 years, never and of course still never, respectively. Leading to the mirror image SaaS metrics rule-of-thumb for churn.

SaaS Metrics Rule-of-Thumb #7
Churn Creates Pressure to Reduce Total Cost of Service

New customer acquisition costs must be paid for by current customers. While growth drives up total acquisition cost, churn erodes the revenue base available to cover it, pushing time to profit out even further. Therefore, churn generates the same pressure to reduce total cost of service as does growth

The harsh judgment of this unbiased, objective SaaS metric math seems pretty clear all around. If you want your SaaS company to ever be profitable, then lower your total cost of service already! These new SaaS metric rules-of-thumb provide the basis of the original claim at the beginning of this post. Only fool-hearty SaaS companies believe that they can grow their way to profitability, it is the recurring revenue mirage that can never be reached, because profitability will only be achieved as growth slows or stops entirely. The combination of growth and profitability requires an extreme discipline of lowering total cost of service through economies-of-scale.

If you are now as bummed as I was when I first fully digested this dilemma, then do not fear! It is always darkest before the dawn. Earlier, we set aside one other viable strategy for accelerating profitability: upselling, or rather increasing recurring revenue without increasing customer acquisition cost or average recurring cost of service. The next post in this series entitled SaaS Recurring Revenue – The Beauty of the Upsell will explore the importance of upselling and upgrades as an essential SaaS strategy to accelerate profitability.

SaaS Metrics Math Notes
The profitability constraints placed on growth and churn by the value of baseline average customer break-even, BE₀, presented above are derived using the previously described continuous SaaS metrics model:

C’(t) = b – aC(t) + gC(t)

With the solution…

C(t) = [ b⁄(g-a) ] x ( e^(g-a)t – 1 )

ΔC_new(t) = b + gC(t)

Time to profit is then calculated by substituting these into the zero profit equation:

[ ARR - ACS ] C(t_profit) – CAC ΔC_new(t_profit) = 0

Giving the following formula for time to profit:

t_profit	=	log [	1 - aBE₀	]
			1 – gBE₀
		g – a

The mathematically inclined can verify that this result satisfies all the principles established in SaaS metrics rules-of-thumb 4 through 7. t_profit increases with both growth and churn commensurate with increasing values of gBE₀ and aBE₀. When gBE₀ ≥ 1 or gBE₀ ≥ 1 no solution exists, diverging to an infinite time to profit as either of these values approaches 1. t_profit ≥ BE₀ always and reduces to t_profit = BE₀ in the limit where a = g = 0.

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Profitability – SaaS Company is as SaaS Customer Does

Joel York — Wed, 10 Feb 2010 02:05:29 +0000

Profitability, one would think, should come quite naturally to a successful, growing SaaS company. But, SaaS startups have consistently struggled to reach profitability. Unprofitable SaaS companies have gone public and remained unprofitable for years after their IPOs, even as they grow revenues into the hundreds of millions of dollars. SaaS companies have eschewed economic convention, with management and investors alike calmly shrugging off the passe’ advice of their obsolete microeconomics professors who cry out in vain from their blackboards that “the role of the firm is to maximize profit.” Many authors have tried to make sense of this morass, two of the more interesting attempts are Bob Warfield’s post entitled Why do SaaS Companies Lose Money Hand Over Fist, and this really cool analysis by Christian Chabot of tech IPOs comparing software companies that were profitable to those that were not profitable at the time of their IPO. And, I’ve certainly made no secret of my own opinion in posts like this one entitled SaaS Failures – The Recurring Revenue Mirage.

This is the third post in a series on SaaS metrics that will unravel the mystery of SaaS profitability (and the lack thereof) from a different angle, a little mathematics. Once and for all we’ll solve the puzzle of why seemingly successful SaaS companies lose money. So, even if you’re not a math junkie like me, please be patient and plough your way through the technical mumbo jumbo. I’ll guarantee some of these results will surprise you!

SaaS profitability is such an important topic that I’m breaking it up into three separate posts (this being the first) that will build on each other by examining three increasingly complex scenarios: 1) stable growth with churn, 2) viral growth, and 3) growth through upselling and upgrades. Along the way, I’ll also introduce a number of new SaaS Metrics Rules-of-Thumb that highlight the impact of customer acquisition costs and recurring cost of service on long term SaaS profitability.
So, let’s get going…

SaaS Metrics Rule-of-Thumb #4
Company Time to Profit Follows Customer Break-Even

This subtle, strikingly simple rule will reverberate through all that comes to follow on SaaS profitability. And, it provides a simple sanity check for predicting when, if ever, your SaaS business will reach profitability. The gist of the rule is that your SaaS recurring revenue over time is nothing more than the sum of its parts. Therefore, the sooner you break even on a single customer, the sooner you will reach profitability as a company.

The chart below visually shows this principle for a SaaS company with a constant rate of customer acquisition eroded by churn which was closely examined in the first post in this SaaS metrics series.

The accumulated recurring contribution of a SaaS company at any time
mirrors the lifetime accumulation of the typical SaaS customer
directly linking company time to profit with customer break-even time.

The average break-even point for an individual customer without churn, “BE₀” is given by the following formula:

BE₀ = CAC ÷ [ ARR - ACS ]

Now let’s compare this simple individual customer SaaS metric with the more complex SaaS metric of company time to profit. At any given time, the rate at which a SaaS company generates profit is given by the following formula:

Profit = P(t) = [ ARR - ACS ] C (t) – CAC ΔC_new(t)

Where C is the number of customers and ΔC_new is the new customer acquisition rate. Time to profit occurs when this expression changes from a negative loss to a positive profit, or when profit is equal to zero.

P(t_profit) = [ ARR - ACS ] C(t_profit) – CAC ΔC_new(t_profit) = 0

If we consider the simplest case scenario of a constant new customer acquisition rate with no churn, “b”, then after time t, we will have b x t total customers, giving the following equation for time to profit:

P(t_profit) = [ ARR - ACS ] x bt_profit – CAC x b = 0

t_profit = CAC ÷ [ ARR - ACS ] = BE₀

In this simplest of cases, time to profit not only follows break-even, it equals break-even. This relationship between company time to profit and break-even time for the single average customer is a completely general principle for SaaS (or any recurring revenue subscription business). Unfortunately, the simple case scenario above is also the best case scenario as spelled out in the next SaaS metrics rule-of-thumb.

SaaS Metrics Rule-of-Thumb #5
Best Case Time to Profit is Simple Break-Even

SaaS Metrics Rule-of-Thumb #2 – New Customer Acquisition Growth Must Outpace Churn states that if you want to break free of the churn limit, then you must increase new customer acquisition to compensate. Unfortunately, this mandate places you neatly between a rock and a hard place with respect to profitability. On the one hand, your customer acquisition costs increase each year, while on the other hand churn is eating away at your ability to break-even on every new customer you bring the door. The net result is that both churn and growth push SaaS profitability out beyond the single customer break-even point in direct relation to their intensity. The higher your churn, the longer it takes to reach profitability. The higher your growth rate, the longer it takes to reach profitability. And, if either your percentage churn rate or growth rate are too high given your customer acquisition cost and recurring contribution, your SaaS business will never reach profitability. The chart below shows this general principle for the simple case of a constant new acquisition rate subject to churn.

For a growing SaaS company subject to churn,
the best case time to profit is the average break-even time for a single customer.
If churn is too high, profitability becomes impossible to achieve.

In the next installment in this series entitled Growing Up Poor – How Foolish SaaS Companies Lose Money, I plan to zero in on the root causes that lead seemingly successful SaaS companies to lose money. And, then move on to the solutions to this obstinate problem. Some of the concepts presented in this post as well as earlier SaaS Metrics Rules-of-Thumb were developed in previous installments in this SaaS Metrics series:

SaaS Metrics Math Notes
SaaS Metrics Rule-of-Thumb #5 – Best Case Time to Profit is Simple Break-Even can be shown using a little math trick. After I show it, I’ll try to explain in plain English to provide a little more insight into its cause (hint: if you don’t like the math, skip to the end). Taking the formula above for time to profit:

P(t_profit) = [ ARR - ACS ] C(t_profit) – CAC ΔC_new(t_profit) = 0

and rewriting it in terms of the baseline break-even time, BE₀ = CAC ÷ [ ARR - ACS ], gives the following:

C(t_profit) = BE₀ x ΔC_new(t_profit)

Because we are following SaaS Metrics Rule-of-Thumb #2 – New Customer Acquisition Growth Must Outpace Churn, the new customer acquisition rate, ΔC_new, is always increasing. Since C is just the sum of all the customers acquired at the lower acquisition rates from earlier times, then had we acquired customers at the current acquisition rate the whole time, we would clearly have acquired more customers.

t x ΔC_new(t) ≥ C(t)

Substituting this little trick formula into the above gives the following:

t_profit x ΔC_new(t_profit) ≥ C(t_profit) = BE₀ x ΔC_new(t_profit)

t_profit ≥ BE₀

What does it mean? In a subscription business, you reach profitability when the contribution from current customers covers the acquisition cost of new customers. This is the essence of SaaS Metric Rule #4 – Company Time to Profit Follows Customer Break-Even. In the base case, everything is constant and customer break-even = company time to profit. But, if you lose customers to churn on the revenue side, while you acquire new customers at a faster rate each year on the cost side, then it takes more current customers to cover your new customers, so the faster you grow, the longer it takes to stack up enough customers to cover your new ones. If you grow too fast, you can never catch up, and you’ll never be profitable…unless you stop growing! Or, you take action to reduce costs. This is the topic of the next installment in the series: Growing Up Poor – How Foolish SaaS Companies Lose Money.

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Metrics – Viral Growth Trumps SaaS Churn

Joel York — Tue, 02 Feb 2010 16:42:46 +0000

Everybody wants their Internet startup to go viral. But, just what does going viral mean? In his book, The Tipping Point, Malcolm Gladwell spells out the mechanics of how ideas spread virally by modeling the roles of key individuals that he calls connectors, mavens and salesmen (highly recommended on the Chaotic Flow blogroll Worthy Reads). When it comes to the Internet, Josh Kopelman eruditely points out that you can’t go viral by bolting it on as a last minute marketing program. You must apply SaaS Top Ten Do #5 and build viral growth into the product.

The aspiration of this post is not to add to the complexity of these theories of viral growth, but to uncover the simplicity of viral growth through a little mathematics. This is the second post in a series on SaaS metrics that explores the impact of viral growth in SaaS using a simple heuristic model with the goal of extending the list of SaaS Metrics Rules of Thumb started in the first post in the series regarding SaaS churn.

The mechanics of viral growth vary greatly by product, customer, market, and even culture. But, the mathematics pretty much boil down to the singular idea expressed so well in the kitschy Faberge shampoo commercial from the 70’s and 80’s.

Viral growth is customer growth that is proportionate to the number of customers.

C_{n + 1} = ( 1 + g ) x C_n

All the connectors, mavens, salesmen and friends are rolled into the little “g” (for growth rate) in the formula above, which states that the number of customers in the time period n + 1 is equal to the number of customers in the time period n, plus a multiple of those same customers. You can think of g as the percentage of friends who actually told two friends who actually then went out and bought some shampoo divided by the amount of time it took them all to complete this circuit.

Like churn, viral growth scales with the number of customers.
When the viral growth rate exceeds the churn rate,
growth explodes through the churn limit.

If you read the previous SaaS metrics post on SaaS churn, you might recognize this formula, because it is identical to the churn formula only the negative churn rate -a has been replaced with the positive viral growth rate g. Thankfully, we can skip over the algebra this time and jump to the solution, simply by replacing -a with (g-a). This quick slight-of-hand gives us the formula for the number of customers in the time period n, C_n, that incorporates viral growth as well as churn.

C_n = b⁄(g-a) x ( ( 1 + g -a )ⁿ -1)

In this formula, ”b” is the baseline constant customer acquisition rate prior to either viral growth or SaaS churn kicking in. The mirror-like relationship between viral growth and SaaS churn in the formula above leads us to our next SaaS metrics rule of thumb.

SaaS Metrics Rule-of-Thumb #3 – Viral Growth Trumps SaaS Churn

The previous SaaS Metrics Rule-of-Thumb #2 claimed that in order to break through the churn limit, new customer acquisition growth must outpace churn. Because churn increases in direct proportion to the number of customers, the surest approach is to drive growth at a higher rate that also increases in proportion to the number of customers, i.e., viraly. Moreover, investors generally expect companies to increase revenue on a percentage basis year over year. Holding products and prices constant, this again requires viral growth of your customer base. Viral growth can come from many sources, but I like to classify it into the following three distinct stages.

Stage 1 Viral Growth – Brute Force Sales and Marketing (small g)

In any given industry, most companies will spend a rather fixed percentage of revenue on sales and marketing, regardless of the size of the company. When the effectiveness of these efforts scales in proportion to the level of spending (which is clearly not always the case), they will drive growth at a rate proportionate to revenue. In a SaaS business, this will also be proportionate to the number of customers. Hence, it is possible to drive growth in proportion to the number of customers simply through nuts and bolts sales and marketing. This stage is arguably not viral growth in the usual marketing sense of the words, but it does meet the strict mathematical definition where customer growth is proportionate to the number of customers. This in fact is the beautiful irony of the Faberge shampoo ad, which was a very effective nuts and bolts marketing campaign, but was probably less successful at getting even one friend to tell one friend about the product, let alone two.

Stage 2 Viral Growth – Customer Advocacy (modest g)

When you are successful at driving word-of-mouth and getting your customers to recommend purchasing your product to new prospects you reach stage 2, the most commonly understood variation of viral growth made famous by the Faberge ad. SaaS customers hold great potential to be advocates, because they confirm their commitment day after day as they continue to use your product and month after month after month as they send in their renewal payments. Driving viral growth through active customer engagement that turns customers into advocates and advocates into evangelists should be high on the agenda of every SaaS marketing plan.

Stage 3 Viral Growth – Ecosystem Buzz (BIG g)

The ecosystem for your product extends beyond your customer base to include all potentially interested parties such as press, analysts, bloggers, social media hounds, partners, vendors, investors, employees, and perhaps even the general public. Most SaaS compaines engage in public relations, tracking down and pitching Mr. Gladwell’s mavens and connectors in the hopes that they will pass the word on to their respective audiences and networks. But, true and lasting stage 3 ecosystem buzz is invariably built upon strong stage 2 customer advocacy. Twitter is a great curent example. If it weren’t for the dedicated and comparatively small group of Silicon Valley Web 2.0 junkies that were the early adopters of Twitter, you would not be able to follow CNN tweets today.

As previously mentioned, this is the second post in this series on SaaS metrics. In the next post, I’ll start adding revenue and costs to the model to explore the impact of viral growth and SaaS churn on SaaS profitability over time.

SaaS Metric Math Notes
The discrete model presented above can also be treated as a continuous model represented by the linear first order differential equation: C’(t) = b – a C(t) + gC(t) with the solution: C(t) = b⁄(g-a) ( e^(g-a)t – 1 ). The graph above is plotted using this continuous solution. In the discrete model, the factors for viral growth and SaaS churn represent change over a specified constant period of time, e.g., a month or year, whereas in the continuous approach they represent instantaneous change.

For a rapidly growing SaaS business, where year over year growth hides the change from quarter to quarter or even month to month, the continuous model is better suited for estimating actual SaaS metrics. From a practical point of view, you just have to be careful not to put overly averaged growth or churn percentages into the formulas, e.g., for the SaaS churn limit b/a. The distinction really only matters for rates of 30% or more, in which case the continuous rate is given by the formula of g_cont = -log(1+g_avg).

Going forward, I’ll be dropping the discrete model entirely and will present only graphical solutions to the continuous model, because the continuous metric model is easier to manage and produces more elegant solutions. But, I will not be presenting the calculus required, so as not to put my loyal readers to sleep. Happy to share it with anyone who asks for it by email.

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Metrics – SaaS Churn Kills SaaS Growth

Joel York — Mon, 01 Feb 2010 20:09:32 +0000

This is the first post in a series on SaaS metrics where I plan to develop a variety of SaaS financial metric models using simple mathematical heuristics. In the process, I hope to highlight important relationships between key SaaS metrics and develop a short list of valuable SaaS Metrics Rules-of-Thumb.

SaaS Metrics Rule-of-Thumb #1 – SaaS Churn Kills SaaS Company Growth

Consider a SaaS company that acquires new customers at the constant rate of “b” (for bookings), and has a percentage churn rate of “a” (for attrition). The number of customers after n periods of time “C_n” is given by the following formula:

C_n+1 = b + ( 1 – a ) x C_n

C_n = b + b (1-a) + b (1-a)² + b (1-a)³ … + b(1-a)^n-2 + b(1-a)^n-1

C_n = b⁄a x ( 1 – ( 1 -a )ⁿ )

This formula can be approximated at the two extremes of early growth and maturity.

Early Growth	C_n = b x n	acquisition rate x time	(n x a << 1)
Maturity Limit	C_limit = b ÷ a	acquisition rate ÷ % churn rate	(n x a >> 1)

These two boundaries are shown in the chart below along with the blue curve representing total customers over time.

As a SaaS company grows, absolute churn increases with the total number of existing customers and will limit growth if new customers are not added at a faster and faster rate.

In the early days, churn is small and the customer base grows unimpeded at the customer acquisition rate. As the customer base grows, the absolute value of churn increases and soon overwhelms new customer acquistion. When the customer acquisition rate, b, equals churn, a x C, then the number of customers coming in the door is exactly equal to the number leaving. At this point further growth is impossible, limiting the total customer base size to the new customer acquisition rate divided by the percentage churn rate. This limit might be more than satisfying if you run a bootstrapped, private SaaS business as your primary means of personal income. But, it is unlikely to satisfy investors if you are a VC-backed SaaS startup, bringing us to…

SaaS Metrics Rule-of-Thumb #2 – New Customer Acquisition Growth Must Outpace Churn

The bad news is that if you want to grow your SaaS company without limits, you can’t just sit back and book a hundred new customers per year and expect recurring revenue to accumulate, because sooner or later churn catches up with you. You must not only acquire new customers, but you must acquire them at an increasing rate that outpaces your increasing churn. The good news is that the lower your percentage churn rate, the longer you have to figure it out, because your growth won’t slow unless your new customer acquisition rate remains constant for a time equal to one divided by the percentage churn rate. But, who wants to acquire customers at a flat rate anyway?

In the next post in this SaaS Metrics series, I’ll explore how viral growth is the surest path (albeit not the only path) to achieve the goal of SaaS Metrics Rule-of-Thumb #2 above and break the chains of SaaS churn.

SaaS Metrics Math Notes
This relationship between SaaS churn and SaaS growth can also be derived (somewhat more cleanly) using a continuous model as a function of time, “t”, rather than a discrete model as a function of the number of periods, n. For those that remember their college calculus, the model is represented by the linear first order differential equation: C’(t) = b – a C(t) with the solution: C(t) = b⁄a ( 1 – e^-at ). The graph above is plotted using this continuous solution. It is also worth mentioning that this SaaS financial metrics model and the future metrics models that will be presented apply not only to SaaS, but to any subscription-based business.

Chaotic Flow by Joel York » SaaS Metrics

SaaS Business Model – On the Cloud, the Customer is King

Customers vs. Copies

Happy Customers Drive SaaS Business Model ROI

Breaking Down Total SaaS Cost of Service

SaaS Customer Lifetime Value Drives SaaS Company Value

SaaS Metrics Rule-of-Thumb #10 SaaS Customer Lifetime Value Drives SaaS Company Value

Check out the rest of the SaaS Metrics Rules-of-Thumb

SaaS Metrics – Joel’s Magic Number for SaaS Companies

SaaS Metrics Rule of Thumb #9 – Joel’s SaaS Magic Number

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SaaS Revenue – The Beauty of Upselling and Upgrades

SaaS Metrics Rule-of-Thumb #8 Upselling and Upgrades Accelerate SaaS Profitability

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Growing Up Poor – How Foolish SaaS Companies Lose Money

SaaS Metrics Rule-of-Thumb #6Growth Creates Pressure to Reduce Total Cost of Service

SaaS Metrics Rule-of-Thumb #7Churn Creates Pressure to Reduce Total Cost of Service

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SaaS Profitability – SaaS Company is as SaaS Customer Does

SaaS Metrics Rule-of-Thumb #4Company Time to Profit Follows Customer Break-Even

SaaS Metrics Rule-of-Thumb #5Best Case Time to Profit is Simple Break-Even

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SaaS Metrics – Viral Growth Trumps SaaS Churn

SaaS Metrics Rule-of-Thumb #3 – Viral Growth Trumps SaaS Churn

Stage 1 Viral Growth – Brute Force Sales and Marketing (small g)

Stage 2 Viral Growth – Customer Advocacy (modest g)

Stage 3 Viral Growth – Ecosystem Buzz (BIG g)

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SaaS Metrics – SaaS Churn Kills SaaS Growth

SaaS Metrics Rule-of-Thumb #1 – SaaS Churn Kills SaaS Company Growth

SaaS Metrics Rule-of-Thumb #2 – New Customer Acquisition Growth Must Outpace Churn

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SaaS Metrics Rule-of-Thumb #10
SaaS Customer Lifetime Value Drives SaaS Company Value

SaaS Metrics Rule-of-Thumb #8
Upselling and Upgrades Accelerate SaaS Profitability

SaaS Metrics Rule-of-Thumb #6
Growth Creates Pressure to Reduce Total Cost of Service

SaaS Metrics Rule-of-Thumb #7
Churn Creates Pressure to Reduce Total Cost of Service

SaaS Metrics Rule-of-Thumb #4
Company Time to Profit Follows Customer Break-Even

SaaS Metrics Rule-of-Thumb #5
Best Case Time to Profit is Simple Break-Even